scholarly journals Noise Removal from EEG Signals in Polisomnographic Records Applying Adaptive Filters in Cascade

10.5772/17219 ◽  
2011 ◽  
Author(s):  
M. Agustina Garces Correa ◽  
Eric Laciar
Keyword(s):  
Adaptive Filters ◽  
Noise Removal ◽  
Eeg Signals

scholarly journals Performance Analysis of Adaptive Filters with various Wavelets for Noise Removal in EEG Signals

2019 ◽  
Vol 9 (2) ◽  
pp. 3071-3076
Keyword(s):  
Adaptive Filters ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Performance Parameters ◽  
Eeg Signals ◽  
Analysis Techniques ◽  
Wrong Diagnosis ◽  
Wavelet Transformations

Noise removal from recorded EEG signal is most essential for better analysis of brain disorders. During recoding time, EEG signals are usually contaminated by various noise and distortions due to several artifacts. These noisy EEG signals may lead to wrong diagnosis of brain disorders. There are several techniques available to remove the noise from EEG signals. But these techniques are unable to remove the noise completely. However, they can minimize the noise in EEG signals so that the physicians can predict brain disorders. This work presents to minimize the noise by Discrete Wavelet Transform Methods using haar, db2, symlet and coiflet wavelets. EEG original signals from public EEG database are used for experimentation and wavelet transformations, are applied by using Matlab code. The filters performance is measured and analyzed on the basis of performance parameters like SNR and MSE which are calculated for various step sizes of signal and filter orders. Wavelet analysis techniques shows better performance when compared to others

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

scholarly journals A nonlinear method of removing harmonic noise in geophysical data

2011 ◽  
Vol 18 (3) ◽  
pp. 367-379 ◽  
Author(s):  
Y. Jeng ◽  
C.-S. Chen
Keyword(s):  
Adaptive Filters ◽  
Signal Reconstruction ◽  
Adaptive Method ◽  
Noise Removal ◽  
Ensemble Empirical Mode Decomposition ◽  
Geophysical Data ◽  
Ocean Bottom ◽  
Nonlinear Method ◽  
Adaptive Data Analysis ◽  
Harmonic Noise

Abstract. A nonlinear, adaptive method to remove the harmonic noise that commonly resides in geophysical data is proposed in this study. This filtering method is based on the ensemble empirical mode decomposition algorithm in conjunction with the logarithmic transform. We present a synthetic model study to investigate the capability of signal reconstruction from the decomposed data, and compare the results with those derived from other 2-D adaptive filters. Applications to the real seismic data acquired by using an ocean bottom seismograph and to a shot gather of the ground penetrating radar demonstrate the robustness of this method. Our work proposes a concept that instead of Fourier-based approaches, the harmonic noise removal in geophysical data can be achieved effectively by using an alternative nonlinear adaptive data analysis method, which has been applied extensively in other scientific studies.

scholarly journals Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mani Adib ◽  
Edmond Cretu
Keyword(s):  
Current Distribution ◽  
Adaptive Filters ◽  
Vestibular Stimulation ◽  
Decomposition Methods ◽  
Galvanic Vestibular Stimulation ◽  
Eeg Signals ◽  
Time Series Regression ◽  
Regression Methods ◽  
Main Challenge ◽  
Novel Method

We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters.

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